1. Field of the Invention
This invention relates to computer-assisted analysis of biological molecules, particularly of biologically active peptides and peptide mimetics.
2. Description of Related Art
With the ever increasing plethora of biological information, the new branch of biological sciences called bioinformatics has become increasingly important. Bioinformatics seeks to translate the mass of protein (polypeptide) sequence information into knowledge of structure and more importantly, function.
One category of peptides where structure and function information would be useful are Class I major histocompatiblity complex (MHC) molecules (in humans, the MHC is called HLA). MHC molecules are cell surface proteins that present bound peptides. These peptides are analyzed by immuno-surveillant cytotoxic T-cells (CTLs) to identify foreign or unhealthy cells for removal. Understanding this process is important, as it constitutes the primary immunological defense against viruses and perhaps tumor causing cells. It is also a major component responsible for transplant rejection. A. Townsend and H. Bodmer, Annu. Rev. Immunol. 7, 601 (1989); J. W. Yewdell and J. R. Binnink, Adv. Immunol. 52, 1 (1992). Since the affinity of the bound peptides largely determines the stability of the expressed class I molecules and their recognition by CTLs, it is crucial to determine the rules of peptide binding by class I molecules. Analyses of peptides eluted from class I MHC molecules reveal that they are short, usually 8-10 amino acids long, with particular amino acids occurring in specific, anchor positions with a very high frequency. Highly conserved pockets accommodate these anchor amino acids as well as the peptide amino and carboxy termini. The carboxy terminal pocket is considerably less constraining than the amino terminus (M. Matsumura, Y. Saito, M. R. Jackson, E. S. Song and P. A. Peterson, J. Biol. Chem. 267(33), 23589 (1992); E. J. Collins, E. N. Garboczi and D. C. Wiley, Nature 371, 629 (1994)), suggesting the possibility of using a phage display analysis for peptide screening.
Binding analyses with synthetic peptides have confirmed the importance of the anchor residues but have also revealed amino acid preferences at other positions. These secondary anchor residues can have profound effects on binding affinities, as peptide binding to human class I molecules can vary by over four orders of magnitude. Furthermore, combinations of anchor amino acids are restricted, making the binding rules complex. Hence predictions based solely on anchor amino acids are at best about 20% accurate. J. Ruppert, J. Sidney, E. Celis, R. T. Kubo, H. M. Grey and A. Sette, Cell 74, 929 (1993). It would be desirable to have an analysis that tests a large number of peptide sequences and considers the correlated effects of amino acids.
Artificial intelligence and pattern recognition methods may prove to be powerful tools in the bioinformatics field. For example, an artificial neural network (ANN) has been successfully applied to predict mitochondrial precursor cleavage sites (G. Schneider, J. Schuchhardt and P. Wrede, Biophys. J 68, 434 (1995)) and membrane-spanning amino acid sequences (R. Lohmann, G. Schanider, D. Behrens and P. Wrede, Protein Science 3,1597 (1994); M. Milik and J. Skolnick, in: "Proceedings of Fourth Annual Conference on Evolutionary Programming", MIT Press, La Jolla (1995)). However, to date, ANN analysis has not been successfully applied to prediction of binding motifs of biologically active peptides and peptide mimetics. The present invention provides a method and system for accomplishing this goal.